o4-mini vs Grok 3

Compare o4-mini and Grok 3. Find out which one is better for your use case.

Model Comparison

Featureo4-miniGrok 3
ProviderOpenAIxAI
Model Typetexttext
Context Window200,000 tokens131,072 tokens
Input Cost$1.10 / 1M tokens$3.00 / 1M tokens
Output Cost$4.40 / 1M tokens$15.00 / 1M tokens

Strengths & Best Use Cases

o4-mini

1. Fast and efficient reasoning

  • Provides strong reasoning capabilities with significantly lower latency and cost compared to larger o-series models.
  • Ideal for lightweight reasoning tasks, logic steps, and quick multi-step thinking.

2. Optimized for coding tasks

  • Performs exceptionally well in code generation, debugging, and explanation.
  • Useful for IDE integrations, coding assistants, and developer tools with tight latency budgets.

3. Strong visual reasoning

  • Accepts image inputs for tasks such as diagram interpretation, charts, UI analysis, and visual logic.
  • Great for hybrid text-image reasoning flows.

4. Large 200K-token context window

  • Capable of processing long documents, multi-file codebases, or extended analysis.
  • Reduces need for chunking or external retrieval pipelines.

5. High 100K-token output limit

  • Supports lengthy reasoning sequences, full codebase explanations, or multi-section documents.

6. Broad API compatibility

  • Available in Chat Completions, Responses, Realtime, Assistants, Batch, Embeddings, and Image workflows.
  • Supports streaming, function calling, structured outputs, and fine-tuning.

7. Cost-efficient for production

  • Lower input/output pricing makes it suitable for large-scale deployments, SaaS products, and recurring tasks.

8. Succeeded by GPT-5 mini

  • GPT-5 mini offers improved speed, reasoning power, and pricing, but o4-mini remains a strong option for cost-sensitive workloads.

Grok 3

1. Strong enterprise-grade reasoning

  • Built for deep logical reasoning, structured decision-making, and multi-step analysis.
  • Performs exceptionally in domains requiring precision: law, finance, healthcare, and STEM.

2. Excellent at data extraction and summarization

  • Optimized for structured extraction from documents, PDFs, tables, and complex text.
  • Ideal for enterprise workflows like reporting, compliance automation, or knowledge mining.

3. High-performance coding capabilities

  • Excels at code generation, debugging, refactoring, and explaining code.
  • Competitive with top-tier coding models for multi-file, long-context code reasoning.

4. Supports function calling and structured outputs

  • Integrates cleanly with agent frameworks and external tools.
  • Predictable, schema-aligned responses suitable for production systems.

5. Large 131K context window

  • Handles long documents, transcripts, contracts, codebases, or multi-document tasks.
  • Useful for ingesting highly technical materials in one pass.

6. Efficient cost structure with cached token pricing

  • Cached inputs: only $0.75 / 1M tokens, enabling large-scale systems.
  • Encourages reuse for powerful retrieval-augmented workflows.

7. Enterprise reliability and availability

  • Supported across multiple regions (us-east-1, eu-west-1).
  • Consistent rate limits: 600 requests/min.
  • Suitable for production-grade apps with stability requirements.

8. Supports advanced search capabilities

  • Optional Live Search add-on for real-time knowledge retrieval.
  • Pricing: $25 per 1K sources.

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